Enzyme improvement in the absence of structural knowledge: A novel statistical approach

Yoram Barak, Yuval Nov, David F. Ackerley, A. Matin

Research output: Contribution to journalArticlepeer-review


Most existing methods for improving protein activity are laborious and costly, as they either require knowledge of protein structure or involve expression and screening of a vast number of protein mutants. We describe here a successful first application of a novel approach, which requires no structural knowledge and is shown to significantly reduce the number of mutants that need to be screened. In the first phase of this study, around 7000 mutants were screened through standard directed evolution, yielding a 230-fold improvement in activity relative to the wild type. Using sequence analysis and site-directed mutagenesis, an additional single mutant was then produced, with 500-fold improved activity. In the second phase, a novel statistical method for protein improvement was used; building on data from the first phase, only 11 targeted additional mutants were produced through site-directed mutagenesis, and the best among them achieved a >1500-fold improvement in activity over the wild type. Thus, the statistical model underlying the experiment was validated, and its predictions were shown to reduce laboratory labor and resources.

Original languageEnglish
Pages (from-to)171-179
Number of pages9
JournalISME Journal
Issue number2
StatePublished - Feb 2008

Bibliographical note

Funding Information:
We are grateful to Drs Bruno Salles, Mike Benoit and Ms Mimi Keyhan for their useful advice and stimulating discussion. We thank Dr Stephen H Thorne for kindly supplying us with freshly made JC breast cancer cells. We also thank three anonymous referees whose insightful comments and suggestions greatly improved this article. This work was supported by Grants DE-FG03-97ER-624940 and DE-FG02-96ER20228 from the Natural and Accelerated Bioremediation Program of US Department of Energy, and Stanford Office of Technology Licensing (1105626-100-WOAAA). YB and DFA were supported, in part, by a Postdoctoral Fellowship from Lady Davis Postdoctoral Fellowship and FRST New Zealand (STAX0101) Fellowship, respectively.


  • Directed evolution
  • Nov-Wein model
  • Protein design
  • Rational design

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Microbiology


Dive into the research topics of 'Enzyme improvement in the absence of structural knowledge: A novel statistical approach'. Together they form a unique fingerprint.

Cite this